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Aims/Introduction: We examined the association between hemoglobin A1c (HbA1c) and anemia, which was categorized into three groups according to mean corpuscular volume (MCV), as well as the association between hemoglobin in the non‐anemic range and HbA1c. Materials and Methods: We used the 2016 health checkup data from 36,422 workers without diabetes. Anemic people were divided into three groups based on MCV: <80, 80–90 and >90 fL. Non‐anemic people were divided into four groups based on their hemoglobin levels. We carried out multiple linear regression models to estimate the means and 95% confidence intervals (CIs) of HbA1c. Results: For men, 0.2% had anemia with MCV <80 fL, 0.5% had anemia with MCV 80–90 fL, 0.9% had anemia with MCV >90 fL and 98.4% had no anemia. For women, the corresponding values were 6.1, 6.4, 2.8 and 84.7%, respectively. The adjusted mean HbA1c (%) values for men with anemia with MCV <80, 80–90 and >90 fL were 5.67 (95% CI 5.60–5.74), 5.58 (95% CI 5.54–5.62) and 5.41 (95% CI 5.37–5.44), respectively. Among men without anemia, HbA1c (%) increased from 5.36 (95% CI 5.34–5.39) in those with hemoglobin ≥17.5 mg/dL to 5.45 (95% CI 5.45–5.46) in those with hemoglobin 13.0 to <14.5 mg/dL (P for trend <0.001). The HbA1c values were higher in men with anemia with MCV <80 fL or MCV 80–90 fL, but lower in men with MCV >90 fL, compared with non‐anemic men with hemoglobin 13.0 to <14.5 mg/dL (All P < 0.001). Similar findings were observed in women. Conclusions: We observed elevated HbA1c among anemic people with MCV <80 fL or MCV 80–90 fL, and decreased HbA1c among anemic people with MCV >90 fL, suggesting that different types of anemia might influence HbA1c differently. In addition, non‐anemic people with lower hemoglobin levels had higher HbA1c levels, suggesting that hemoglobin levels are in need of consideration when interpreting HbA1c values among non‐anemic people. [ABSTRACT FROM AUTHOR]

Aims/Introduction: We examined a prospective association between serum creatinine levels and diabetes. Materials and Methods: The present study included 31,343 male workers without diabetes, and aged between 20 and 64 years at baseline. We calculated the cumulative average of their serum creatinine over the study period. We defined diabetes as either glycated hemoglobin levels ≥6.5%, random glucose levels ≥200 mg/dL, fasting glucose levels ≥126 mg/dL or receiving antidiabetic treatment. Cox proportional hazards regression analysis was carried out to estimate the hazard ratio (HR) and 95% confidence interval (CI). Results: With a median observation of 7.7 years, 2,509 participants developed diabetes. After adjusting for age, smoking, body mass index, hypertension and dyslipidemia, lower cumulative average serum creatinine levels were related to a greater diabetes risk: HRs were 1.56 (95% CI 1.35–1.82), 1.22 (1.09–1.35) and 1.06 (0.96–1.17) for the participants with serum creatinine <0.70, 0.70–0.79 and 0.80–0.89 mg/dL, respectively, compared with those with 0.90–1.20 mg/dL (P for trend <0.001). The serum creatinine‐diabetes association was more pronounced among older adults (serum creatinine <0.70 vs 0.90–1.20 mg/dL, HR 1.66, 95% CI 1.37–2.00) than younger adults (HR 1.32, 95% CI 1.02–1.71; P for interaction by age group = 0.001). Conclusions: Low serum creatinine is associated with an increased risk of diabetes. Screening serum creatinine levels can be used to identify those who are at high risk of diabetes. [ABSTRACT FROM AUTHOR]

Abstract: Aims/Introduction: We previously developed a 3‐year diabetes risk score in the working population. The objective of the present study was to develop and validate flexible risk models that can predict the risk of diabetes for any arbitrary time‐point during 7 years. Materials and Methods: The participants were 46,198 Japanese employees aged 30–59 years, without diabetes at baseline and with a maximum follow‐up period of 8 years. Incident diabetes was defined according to the American Diabetes Association criteria. With routine health checkup data (age, sex, abdominal obesity, body mass index, smoking status, hypertension status, dyslipidemia, glycated hemoglobin and fasting plasma glucose), we developed non‐invasive and invasive risk models based on the Cox proportional hazards regression model among a random two‐thirds of the participants, and used another one‐third for validation. Results: The range of the area under the receiver operating characteristic curve increased from 0.73 (95% confidence interval 0.72–0.74) for the non‐invasive prediction model to 0.89 (95% confidence interval 0.89–0.90) for the invasive prediction model containing dyslipidemia, glycated hemoglobin and fasting plasma glucose. The invasive models showed improved integrated discrimination and reclassification performance, as compared with the non‐invasive model. Calibration appeared good between the predicted and observed risks. These models performed well in the validation cohort. Conclusions: The present non‐invasive and invasive models for the prediction of diabetes risk up to 7 years showed fair and excellent performance, respectively. The invasive models can be used to identify high‐risk individuals, who would benefit greatly from lifestyle modification for the prevention or delay of diabetes. [ABSTRACT FROM AUTHOR]